An example of a natural language processing system is an automated machine translation (MT) system. Such automated machine translation systems translate text in a first language to text in a second language. Certain automated machine translation systems share the following framework. The systems use word-alignments that are produced by models such as translation models. Rules are extracted from a word-aligned corpus using heuristics. A log-linear model combines commonly used features whose weights are optimized on a development set (e.g., a handful of sentences) using reference translations for automatic machine translation evaluation metrics. A beam-search decoder is used to generate final translations.
Optimizing machine translation parameters (i.e., wherein the parameters are weights associated with the features used in the log-linear model) has been shown to be a piece-wise linear problem, and algorithms such as those employing minimum error rate training techniques have been widely applied.
The translation quality of the output text of a machine translation system is typically measured via automatic metrics including BLEU (Bilingual Evaluation Understudy), TER (Translation Edit Rate), WER (Word Error Rate), METEOR (Metric for Evaluation of Translation with Explicit Ordering), n-gram precisions, and their variants. The automatic evaluation metrics are computed on the given source sentences and their human translation references.